Facial Keypoints Detection

• The marking of important areas of the face, such as the eyes, corners of the mouth, and nose, that are relevant for a variety of tasks, such as face filters, emotion recognition, and pose recognition, using convolutional neural network and computer vision techniques to perform facial Keypoint detection.

• It entails forecasting the Facial Key points coordinates for a particular face, such as nose tip, the center of eyes, and so on. To recognize facial key points, we use a Convolutional Neural Network-based model.

• Convolutional Neural Networks (CNN) has a deep structure that allows them to extract high-level information and provide better precision when identifying each important point. Convolutional networks are designed to anticipate all points at the same time.

Import Required Libraries

import os
import cv2
import time
import math
import keras
import tensorflow
from PIL import Image
import pandas as pd 
import numpy as np
import missingno as msno
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.utils import shuffle
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, GlobalAveragePooling2D, Activation
from keras.layers import Flatten, Dense
from keras.layers import normalization
from tensorflow.keras.layers import BatchNormalization
from keras import optimizers
from keras.callbacks import ModelCheckpoint, History

Importing train & test dataset

train = pd.read_csv('training.csv', header=0)
test = pd.read_csv('test.csv', header=0)
X_test, y_test = test[:-1], test["Image"]

len(train)

7049

train.T.head()

	0	1	2	3	4	5	6	7	8	9	...	7039	7040	7041	7042	7043	7044	7045	7046	7047	7048
left_eye_center_x	66.033564	64.332936	65.057053	65.225739	66.725301	69.680748	64.131866	67.468893	65.80288	64.121231	...	69.229935	63.352951	65.711151	67.929319	66.867222	67.402546	66.1344	66.690732	70.965082	66.938311
left_eye_center_y	39.002274	34.970077	34.909642	37.261774	39.621261	39.968748	34.29004	39.413452	34.7552	36.740308	...	38.575634	35.671311	38.843545	35.846552	37.356855	31.842551	38.365501	36.845221	39.853666	43.42451
right_eye_center_x	30.227008	29.949277	30.903789	32.023096	32.24481	29.183551	29.578953	29.355961	27.47584	29.468923	...	29.407912	33.952078	32.268751	28.68782	30.750926	29.746749	30.478626	31.66642	30.543285	31.096059
right_eye_center_y	36.421678	33.448715	34.909642	37.261774	38.042032	37.563364	33.13804	39.621717	36.1856	38.390154	...	38.34545	40.816448	37.706043	41.452484	40.115743	38.632942	39.950198	39.685042	40.772339	39.528604
left_eye_inner_corner_x	59.582075	58.85617	59.412	60.003339	58.56589	62.864299	57.797154	59.554951	58.65216	58.620923	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 7049 columns

Check for null values

print(f'Feature \t\t\t Missing \t Percentage missing\n')
for k,v in train.isna().sum().items():
    print(f'{k !s:30} :{v :8} \t {round(v/len(train),2)}%')

Feature 			 Missing 	 Percentage missing

left_eye_center_x              :      10 	 0.0%
left_eye_center_y              :      10 	 0.0%
right_eye_center_x             :      13 	 0.0%
right_eye_center_y             :      13 	 0.0%
left_eye_inner_corner_x        :    4778 	 0.68%
left_eye_inner_corner_y        :    4778 	 0.68%
left_eye_outer_corner_x        :    4782 	 0.68%
left_eye_outer_corner_y        :    4782 	 0.68%
right_eye_inner_corner_x       :    4781 	 0.68%
right_eye_inner_corner_y       :    4781 	 0.68%
right_eye_outer_corner_x       :    4781 	 0.68%
right_eye_outer_corner_y       :    4781 	 0.68%
left_eyebrow_inner_end_x       :    4779 	 0.68%
left_eyebrow_inner_end_y       :    4779 	 0.68%
left_eyebrow_outer_end_x       :    4824 	 0.68%
left_eyebrow_outer_end_y       :    4824 	 0.68%
right_eyebrow_inner_end_x      :    4779 	 0.68%
right_eyebrow_inner_end_y      :    4779 	 0.68%
right_eyebrow_outer_end_x      :    4813 	 0.68%
right_eyebrow_outer_end_y      :    4813 	 0.68%
nose_tip_x                     :       0 	 0.0%
nose_tip_y                     :       0 	 0.0%
mouth_left_corner_x            :    4780 	 0.68%
mouth_left_corner_y            :    4780 	 0.68%
mouth_right_corner_x           :    4779 	 0.68%
mouth_right_corner_y           :    4779 	 0.68%
mouth_center_top_lip_x         :    4774 	 0.68%
mouth_center_top_lip_y         :    4774 	 0.68%
mouth_center_bottom_lip_x      :      33 	 0.0%
mouth_center_bottom_lip_y      :      33 	 0.0%
Image                          :       0 	 0.0%

Train data

import missingno as msno
msno.bar(train,color=(1, 0, 0))

<Axes: >

Test data

import missingno as msno
msno.bar(test,color=(0.98, 0.28, 0.39))

<Axes: >

# to remove rows with missing values
train.dropna(inplace = True)

# sum of all missing values
train.isnull().sum().sum()

0

# to display summary of dataset structure and content
train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2140 entries, 0 to 2283
Data columns (total 31 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   left_eye_center_x          2140 non-null   float64
 1   left_eye_center_y          2140 non-null   float64
 2   right_eye_center_x         2140 non-null   float64
 3   right_eye_center_y         2140 non-null   float64
 4   left_eye_inner_corner_x    2140 non-null   float64
 5   left_eye_inner_corner_y    2140 non-null   float64
 6   left_eye_outer_corner_x    2140 non-null   float64
 7   left_eye_outer_corner_y    2140 non-null   float64
 8   right_eye_inner_corner_x   2140 non-null   float64
 9   right_eye_inner_corner_y   2140 non-null   float64
 10  right_eye_outer_corner_x   2140 non-null   float64
 11  right_eye_outer_corner_y   2140 non-null   float64
 12  left_eyebrow_inner_end_x   2140 non-null   float64
 13  left_eyebrow_inner_end_y   2140 non-null   float64
 14  left_eyebrow_outer_end_x   2140 non-null   float64
 15  left_eyebrow_outer_end_y   2140 non-null   float64
 16  right_eyebrow_inner_end_x  2140 non-null   float64
 17  right_eyebrow_inner_end_y  2140 non-null   float64
 18  right_eyebrow_outer_end_x  2140 non-null   float64
 19  right_eyebrow_outer_end_y  2140 non-null   float64
 20  nose_tip_x                 2140 non-null   float64
 21  nose_tip_y                 2140 non-null   float64
 22  mouth_left_corner_x        2140 non-null   float64
 23  mouth_left_corner_y        2140 non-null   float64
 24  mouth_right_corner_x       2140 non-null   float64
 25  mouth_right_corner_y       2140 non-null   float64
 26  mouth_center_top_lip_x     2140 non-null   float64
 27  mouth_center_top_lip_y     2140 non-null   float64
 28  mouth_center_bottom_lip_x  2140 non-null   float64
 29  mouth_center_bottom_lip_y  2140 non-null   float64
 30  Image                      2140 non-null   object 
dtypes: float64(30), object(1)
memory usage: 535.0+ KB

train['Image'].head()

0    238 236 237 238 240 240 239 241 241 243 240 23...
1    219 215 204 196 204 211 212 200 180 168 178 19...
2    144 142 159 180 188 188 184 180 167 132 84 59 ...
3    193 192 193 194 194 194 193 192 168 111 50 12 ...
4    147 148 160 196 215 214 216 217 219 220 206 18...
Name: Image, dtype: object

len(train)

2140

# to set a new index for the "train" dataframe
keys = np.arange(0, 2140)
train = train.set_index(keys)
train.T.head(5)

	0	1	2	3	4	5	6	7	8	9	...	2130	2131	2132	2133	2134	2135	2136	2137	2138	2139
left_eye_center_x	66.033564	64.332936	65.057053	65.225739	66.725301	69.680748	64.131866	67.468893	65.80288	64.121231	...	66.827593	64.126581	63.738273	64.644616	64.383649	67.180378	65.72449	68.430866	64.15218	66.683755
left_eye_center_y	39.002274	34.970077	34.909642	37.261774	39.621261	39.968748	34.29004	39.413452	34.7552	36.740308	...	30.620361	33.096095	34.407682	34.280084	35.104561	35.816373	36.30102	38.651975	30.691592	34.483429
right_eye_center_x	30.227008	29.949277	30.903789	32.023096	32.24481	29.183551	29.578953	29.355961	27.47584	29.468923	...	25.111875	25.716503	26.854206	28.284307	30.424912	33.239956	25.377551	28.895857	27.000898	30.78449
right_eye_center_y	36.421678	33.448715	34.909642	37.261774	38.042032	37.563364	33.13804	39.621717	36.1856	38.390154	...	33.298484	38.118015	39.145823	38.586911	33.399298	34.921932	37.311224	37.617027	40.868082	38.578939
left_eye_inner_corner_x	59.582075	58.85617	59.412	60.003339	58.56589	62.864299	57.797154	59.554951	58.65216	58.620923	...	58.383626	57.887226	56.297606	57.75651	57.814386	59.347973	58.530612	61.65935	56.505624	59.255347

5 rows × 2140 columns

type(train)

pandas.core.frame.DataFrame

# list of lists(contains pixel values for images)
imag = []  
for i in range(len(train)):
    if i==210 or i==350 or i==499 or i==512 or i==810 or i==839 or i==895 or i==1058 or i==1194:
        continue
    img = train['Image'][i].split(' ')
    img = [pixel if pixel != '' else '0' for pixel in img]
   
    imag.append(img)

image_list = np.array(imag, dtype = 'float')
X_train = image_list.reshape(-1, 96, 96, 1)
targets = np.array(train.iloc[:, :-1])

print('Shape of images',X_train.shape)
print('Shape of targets',targets.shape)

Shape of images (2131, 96, 96, 1)
Shape of targets (2140, 30)

Data Visualization

#  Visualize
viz = np.array([train['Image'][i].split(' ') for i in range(len(train))],dtype='float')
keys = train.drop(['Image'], axis=1)

# Gallery using Matplotlib 
fig, ax = plt.subplots(5, 5, figsize = (12,12), dpi = 100)
axes = ax.ravel()

for idx,ax  in enumerate(axes):
    ax.imshow(viz[idx].reshape(96, 96, 1))
    photo_visualize_pnts = keys.iloc[idx].values
fig.show()

C:\Users\PRITI CHAUDHARY\AppData\Local\Temp\ipykernel_3592\894729145.py:8: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
  fig.show()

# Gallery with keypoints marking 
fig, ax = plt.subplots(5, 5, figsize = (12,12), dpi = 100)
axes = ax.ravel()

for idx,ax  in enumerate(axes):
    ax.imshow(viz[idx].reshape(96, 96, 1))
    photo_visualize_pnts = keys.iloc[idx].values
    ax.scatter(photo_visualize_pnts[0::2], photo_visualize_pnts[1::2], c='Red', marker='*')
fig.show()

C:\Users\PRITI CHAUDHARY\AppData\Local\Temp\ipykernel_3592\1352036133.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
  fig.show()

Augmentation

We can add different augmenations like -

• flipping
• rotation
• cropping
• adding noise
• bluring
• brightness

etc..

# lets create an util to display the augumentation
def display_augmentation(img, feat, img_f, feat_f):
    plt.figure(figsize=(8,8))
    plt.subplot(2,2,1)
    plt.scatter(feat[0::2],-feat[1::2],c='r',marker='x')
    plt.subplot(2,2,2)
    plt.scatter(feat_f[0::2],-feat_f[1::2],c='r',marker='x')
    plt.subplot(2,2,3)
    display_images(img, feat)
    plt.subplot(2,2,4)
    display_images(img_f, feat_f)

Flipping

# an util that give flipped images and targets
def flipping_augmentation(images, features):
    flipped_images = np.flip(images, axis=2)
    
    flipped_features = features.copy()
    for i, feat in enumerate(flipped_features):
        for j, val in enumerate(feat):
            if j%2==0:
                flipped_features[i][j] = 96-val
            
    return flipped_images, flipped_features

# to create an object to keep track of the augmentations
augmentation_functions = {
    'flip' : flipping_augmentation
}

Cropping

# lets make an util that give us cropped images and targets
def crop_augmentation(images, targets):
    cropped_images = images.copy()

    for i in range(len(images)):
        cropped_images[i,:,:10] = 0
        cropped_images[i,:,86:] = 0
        cropped_images[i,:10,:] = 0
        cropped_images[i,86:,:] = 0

    return cropped_images, targets


augmentation_functions['crop']=crop_augmentation
    

Brightness

# to create an util that adds brightness
def brightness_augmentation(images, features, factor=1.5):
    bright = []
    for img in images:
        bright.append(np.clip(img*factor, 0, 255))
    return np.array(bright), features

augmentation_functions['brightness'] = brightness_augmentation

Adding noise

# to create an utility that adds noise to the image
def noise_augmentation(images, features, factor):
    augmented = []
    noise = np.random.randint(low=0, high=255, size=images.shape[1:])
    for img in images:
        img = img + (noise*factor)
        augmented.append(img)
    
    return np.array(augmented), features

augmentation_functions['noise'] = noise_augmentation

Lets prepare our training data

print('Shape of image data', X_train.shape)
print('Shape of target data', targets.shape)

Shape of image data (2131, 96, 96, 1)
Shape of target data (2140, 30)

# ADDING AUGMENTATION

def augmentation(img, feat , method):
    aug_img, aug_feat = method
    img = np.concatenate([img,aug_img])
    feat = np.concatenate([feat,aug_feat])
    return img, feat


# flip
method = flipping_augmentation(X_train, targets)
augmented_images, augmented_targets = augmentation(X_train, targets, method)
print('image augmentation : flipping')

# crop
method = crop_augmentation(X_train, targets)
augmented_images, augmented_targets = augmentation(augmented_images, augmented_targets, method)
print('image augmentation : cropping')

# brightness
method = brightness_augmentation(X_train, targets, factor=2.0)
augmented_images, augmented_targets = augmentation(augmented_images, augmented_targets, method)
print('image augmentation : adding brightness')


# noise
method = noise_augmentation(X_train, targets, factor=0.2)
augmented_images, augmented_targets = augmentation(augmented_images, augmented_targets, method)
print('image augmentation : adding noise')

image augmentation : flipping
image augmentation : cropping
image augmentation : adding brightness
image augmentation : adding noise

print('Shape of data after augmentation')
print('Shape of image data',augmented_images.shape)
print('Shape of target data', augmented_targets.shape)

Shape of data after augmentation
Shape of image data (10655, 96, 96, 1)
Shape of target data (10700, 30)

# to create an util that will display the images
def display_images(img, feat):
    plt.imshow(img, cmap=plt.cm.gray);
    plt.scatter(feat[0::2], feat[1::2], c='r', marker='x')
    

# to check our data one last time before we start building models

def visualize_data(images, targets):
    plt.figure(figsize=(12,12))
    for i in range(10):
        idx = np.random.randint(images.shape[0])
        plt.subplot(2,5,i+1)
        display_images(images[idx], targets[idx])
        plt.axis('off')
    plt.subplots_adjust(bottom=0.5)
    plt.show()

visualize_data(augmented_images, augmented_targets)

Modeling

pretrained_model = tf.keras.applications.mobilenet_v2.MobileNetV2(input_shape=(96,96,3),include_top=False,weights='imagenet')

%%time
augmented_images = tf.keras.applications.mobilenet_v2.preprocess_input(augmented_images)

CPU times: total: 250 ms
Wall time: 236 ms

%%time

# to check our data to make sure everything is fine
visualize_data(augmented_images,augmented_targets)

CPU times: total: 609 ms
Wall time: 571 ms

# to create TensorFlow datasets from NumPy arrays to efficiently feed data to our model
images_ds = tf.data.Dataset.from_tensor_slices(augmented_images)
targets_ds = tf.data.Dataset.from_tensor_slices(augmented_targets)

ds = tf.data.Dataset.zip((images_ds, targets_ds))
ds = ds.shuffle(buffer_size=augmented_targets.shape[0])
ds = ds.batch(64)
ds = ds.prefetch(tf.data.AUTOTUNE)

# to split a TensorFlow dataset "ds" into two subsets for training and validation
train_ds = ds.skip(10).shuffle(100)
val_ds = ds.take(10)

FFNN

# to create a preprocessing layer
class ImageTile(tf.keras.layers.Layer):
    def __init__(self):
        super().__init__(trainable = False)
        
    def call(self, inputs):
        return tf.tile(inputs,tf.constant([1,1,1,3]))

def FFNN(units):
    return tf.keras.Sequential([
        tf.keras.layers.Dense(units),  
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Activation('relu'),
    ])

block = []
for units in [512,256,128,64]:
    block.append(FFNN(units))

model = tf.keras.Sequential([
    tf.keras.Input(shape=(96,96,1)),
    ImageTile(),
    pretrained_model,
    tf.keras.layers.GlobalMaxPooling2D(),
    *block,
    tf.keras.layers.Dense(30)])

model.layers[1].trainable=False

model.compile(optimizer='adam',
              loss=tf.keras.losses.MeanSquaredError(),
              metrics=['accuracy', 'mae', 'mse'])

model.summary()

Model: "sequential_4"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 image_tile (ImageTile)      (None, 96, 96, 3)         0         
                                                                 
 mobilenetv2_1.00_96 (Functi  (None, 3, 3, 1280)       2257984   
 onal)                                                           
                                                                 
 global_max_pooling2d (Globa  (None, 1280)             0         
 lMaxPooling2D)                                                  
                                                                 
 sequential (Sequential)     (None, 512)               657920    
                                                                 
 sequential_1 (Sequential)   (None, 256)               132352    
                                                                 
 sequential_2 (Sequential)   (None, 128)               33408     
                                                                 
 sequential_3 (Sequential)   (None, 64)                8512      
                                                                 
 dense_4 (Dense)             (None, 30)                1950      
                                                                 
=================================================================
Total params: 3,092,126
Trainable params: 832,222
Non-trainable params: 2,259,904
_________________________________________________________________

# decaying learing rate
def decay_lr(epoch):
  return 0.01*math.pow(0.77,epoch)

lr_schedule = tf.keras.callbacks.LearningRateScheduler(decay_lr)
lr_on_plateau = tf.keras.callbacks.ReduceLROnPlateau(patience=2)
early_stopping = tf.keras.callbacks.EarlyStopping(
                        monitor='val_loss',
                        patience=3,
                        restore_best_weights=True)

Fit / Train the model

history = model.fit(train_ds, epochs=20, 
                    validation_data=val_ds, 
                    callbacks=[early_stopping,lr_on_plateau])

Epoch 1/20
157/157 [==============================] - 47s 236ms/step - loss: 2331.0710 - accuracy: 0.0537 - mae: 44.4892 - mse: 2331.0710 - val_loss: 2080.5728 - val_accuracy: 0.0828 - val_mae: 41.6663 - val_mse: 2080.5728 - lr: 0.0010
Epoch 2/20
157/157 [==============================] - 36s 230ms/step - loss: 1527.6349 - accuracy: 0.0509 - mae: 34.3936 - mse: 1527.6349 - val_loss: 1342.0967 - val_accuracy: 0.0234 - val_mae: 31.8816 - val_mse: 1342.0967 - lr: 0.0010
Epoch 3/20
157/157 [==============================] - 35s 223ms/step - loss: 752.5648 - accuracy: 0.0156 - mae: 21.8043 - mse: 752.5648 - val_loss: 713.2049 - val_accuracy: 0.0359 - val_mae: 21.4781 - val_mse: 713.2049 - lr: 0.0010
Epoch 4/20
157/157 [==============================] - 36s 230ms/step - loss: 315.2984 - accuracy: 0.0290 - mae: 12.8013 - mse: 315.2984 - val_loss: 224.0136 - val_accuracy: 0.0312 - val_mae: 10.7055 - val_mse: 224.0136 - lr: 0.0010
Epoch 5/20
157/157 [==============================] - 35s 222ms/step - loss: 155.3076 - accuracy: 0.0619 - mae: 8.6692 - mse: 155.3076 - val_loss: 118.5531 - val_accuracy: 0.0547 - val_mae: 7.3846 - val_mse: 118.5531 - lr: 0.0010
Epoch 6/20
157/157 [==============================] - 35s 224ms/step - loss: 104.5487 - accuracy: 0.1534 - mae: 6.6954 - mse: 104.5487 - val_loss: 95.2197 - val_accuracy: 0.3016 - val_mae: 6.0651 - val_mse: 95.2197 - lr: 0.0010
Epoch 7/20
157/157 [==============================] - 35s 225ms/step - loss: 87.0340 - accuracy: 0.3460 - mae: 5.7469 - mse: 87.0340 - val_loss: 81.8354 - val_accuracy: 0.4766 - val_mae: 5.4792 - val_mse: 81.8354 - lr: 0.0010
Epoch 8/20
157/157 [==============================] - 36s 226ms/step - loss: 78.0451 - accuracy: 0.6149 - mae: 5.2339 - mse: 78.0451 - val_loss: 74.2922 - val_accuracy: 0.7094 - val_mae: 5.1259 - val_mse: 74.2922 - lr: 0.0010
Epoch 9/20
157/157 [==============================] - 35s 223ms/step - loss: 71.6554 - accuracy: 0.6928 - mae: 4.9463 - mse: 71.6554 - val_loss: 63.5832 - val_accuracy: 0.6734 - val_mae: 4.7148 - val_mse: 63.5832 - lr: 0.0010
Epoch 10/20
157/157 [==============================] - 35s 225ms/step - loss: 64.5646 - accuracy: 0.6892 - mae: 4.6438 - mse: 64.5646 - val_loss: 60.2858 - val_accuracy: 0.6766 - val_mae: 4.4950 - val_mse: 60.2858 - lr: 0.0010
Epoch 11/20
157/157 [==============================] - 36s 228ms/step - loss: 58.2672 - accuracy: 0.6944 - mae: 4.3711 - mse: 58.2672 - val_loss: 49.9047 - val_accuracy: 0.6797 - val_mae: 4.1954 - val_mse: 49.9047 - lr: 0.0010
Epoch 12/20
157/157 [==============================] - 36s 231ms/step - loss: 52.5858 - accuracy: 0.6943 - mae: 4.1293 - mse: 52.5858 - val_loss: 51.4359 - val_accuracy: 0.7219 - val_mae: 4.1679 - val_mse: 51.4359 - lr: 0.0010
Epoch 13/20
157/157 [==============================] - 37s 233ms/step - loss: 48.9068 - accuracy: 0.6927 - mae: 3.9787 - mse: 48.9068 - val_loss: 46.0818 - val_accuracy: 0.6922 - val_mae: 3.7915 - val_mse: 46.0818 - lr: 0.0010
Epoch 14/20
157/157 [==============================] - 35s 223ms/step - loss: 43.3562 - accuracy: 0.6949 - mae: 3.7269 - mse: 43.3562 - val_loss: 36.5814 - val_accuracy: 0.6594 - val_mae: 3.4196 - val_mse: 36.5814 - lr: 0.0010
Epoch 15/20
157/157 [==============================] - 36s 228ms/step - loss: 41.3437 - accuracy: 0.6873 - mae: 3.6517 - mse: 41.3437 - val_loss: 35.2932 - val_accuracy: 0.6750 - val_mae: 3.4177 - val_mse: 35.2932 - lr: 0.0010
Epoch 16/20
157/157 [==============================] - 38s 243ms/step - loss: 34.9885 - accuracy: 0.6814 - mae: 3.4053 - mse: 34.9885 - val_loss: 35.6461 - val_accuracy: 0.6750 - val_mae: 3.4199 - val_mse: 35.6461 - lr: 0.0010
Epoch 17/20
157/157 [==============================] - 36s 230ms/step - loss: 32.0750 - accuracy: 0.6788 - mae: 3.2870 - mse: 32.0750 - val_loss: 27.9889 - val_accuracy: 0.6766 - val_mae: 3.1883 - val_mse: 27.9889 - lr: 0.0010
Epoch 18/20
157/157 [==============================] - 35s 225ms/step - loss: 30.5303 - accuracy: 0.6784 - mae: 3.2136 - mse: 30.5303 - val_loss: 24.0346 - val_accuracy: 0.6687 - val_mae: 2.8760 - val_mse: 24.0346 - lr: 0.0010
Epoch 19/20
157/157 [==============================] - 35s 224ms/step - loss: 27.1651 - accuracy: 0.6802 - mae: 3.0853 - mse: 27.1651 - val_loss: 20.1993 - val_accuracy: 0.7000 - val_mae: 2.8337 - val_mse: 20.1993 - lr: 0.0010
Epoch 20/20
157/157 [==============================] - 37s 234ms/step - loss: 25.6911 - accuracy: 0.6714 - mae: 3.0308 - mse: 25.6911 - val_loss: 21.2234 - val_accuracy: 0.7031 - val_mae: 2.7921 - val_mse: 21.2234 - lr: 0.0010

test.head()

	ImageId	Image
0	1	182 183 182 182 180 180 176 169 156 137 124 10...
1	2	76 87 81 72 65 59 64 76 69 42 31 38 49 58 58 4...
2	3	177 176 174 170 169 169 168 166 166 166 161 14...
3	4	176 174 174 175 174 174 176 176 175 171 165 15...
4	5	50 47 44 101 144 149 120 58 48 42 35 35 37 39 ...

# to processes the images from the test dataset to a format suitable for the input of the facial keypoint detection model.
def get_images(test):   
    image = []
    for i in range(0, len(test)):
        img = test['Image'][i].split(' ')
        img = ['0' if x=='' else x for x in img]
        image.append(img)

    image_list = np.array(image, dtype = 'float')
    test_images = image_list.reshape(-1, 96, 96, 1)
    
    return test_images

test_images = get_images(test)
test_images = tf.keras.applications.mobilenet_v2.preprocess_input(test_images)
test_ds = tf.data.Dataset.from_tensor_slices((test_images)).batch(64)

Predictions

test_preds = model.predict(test_ds)

28/28 [==============================] - 8s 184ms/step

print('Shape of test predictions', test_preds.shape)

Shape of test predictions (1783, 30)

display_images(test_images[0],test_preds[0])

visualize_data(test_images,test_preds)

visualize_data(test_images,test_preds) 

CNN

CONV: Convolutional kernel layer

RELU: Activation function

POOL: Dimension reduction layer

FC: Fully connection layer

Convolutional kernel

Feature extraction

Activation function

to introduce non-linearity
to make network learn more complex representations

Rectified linear unit，ReLU

Pooling layer

Dimensionality reduction
Makes network more computationally efficient
Prevent Overfitting

Repeat 1-3 to create deep network

Fully connected layer

Make predictions about the images using features learned by the Covolutional layers

def data_loader():
    
    # Load dataset file
    data_frame = pd.read_csv('training.csv')
    
    data_frame['Image'] = data_frame['Image'].apply(lambda i: np.fromstring(i, sep=' '))
    data_frame = data_frame.dropna()  # Get only the data with 15 keypoints
   
    # Extract Images pixel values
    imgs_array = np.vstack(data_frame['Image'].values)/ 255.0
    imgs_array = imgs_array.astype(np.float32)    # Normalize, target values to (0, 1)
    imgs_array = imgs_array.reshape(-1, 96, 96, 1)
        
    # Extract labels (key point cords)
    labels_array = data_frame[data_frame.columns[:-1]].values
    labels_array = (labels_array - 48) / 48    # Normalize, traget cordinates to (-1, 1)
    labels_array = labels_array.astype(np.float32) 
    
    # shuffle the train data
# imgs_array, labels_array = shuffle(imgs_array, labels_array, random_state=9)  
    
    return imgs_array, labels_array

# # to check/verify data
# imgs, labels = data_loader()
# print(imgs.shape)
# print(labels.shape)

# n=0
# labels[n] = (labels[n]*48)+48
# image = np.squeeze(imgs[n])
# plt.imshow(image, cmap='gray')
# plt.plot(labels[n][::2], labels[n][1::2], 'ro')
# plt.show()

Building CNN model

# Main model
def the_model():
    model = Sequential()
    
    model.add(Conv2D(16, (3,3), padding='same', activation='relu', input_shape=X_train.shape[1:])) # Input shape: (96, 96, 1)
    model.add(MaxPooling2D(pool_size=2))
    
    model.add(Conv2D(32, (3,3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    
    model.add(Conv2D(64, (3,3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    
    model.add(Conv2D(128, (3,3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    
    model.add(Conv2D(256, (3,3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    
    # Convert all values to 1D array
    model.add(Flatten())
    
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.2))

    model.add(Dense(30))
    
    return model

X_train, y_train = data_loader()
print("Training datapoint shape: X_train.shape:{}".format(X_train.shape))
print("Training labels shape: y_train.shape:{}".format(y_train.shape))

Training datapoint shape: X_train.shape:(2140, 96, 96, 1)
Training labels shape: y_train.shape:(2140, 30)

epochs = 60
batch_size = 64

model = the_model()
hist = History()

checkpointer = ModelCheckpoint(filepath='checkpoint1.hdf5', 
                               verbose=1, save_best_only=True)

# Compile Model
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])

Model fitting

model_fit = model.fit(X_train, y_train, validation_split=0.2, epochs=epochs, batch_size=batch_size, callbacks=[checkpointer, hist], verbose=1)

model.save('model1.h5')

Epoch 1/60
27/27 [==============================] - ETA: 0s - loss: 0.0266 - accuracy: 0.5164
Epoch 1: val_loss improved from inf to 0.01006, saving model to checkpoint1.hdf5
27/27 [==============================] - 10s 334ms/step - loss: 0.0266 - accuracy: 0.5164 - val_loss: 0.0101 - val_accuracy: 0.4322
Epoch 2/60
27/27 [==============================] - ETA: 0s - loss: 0.0077 - accuracy: 0.6893
Epoch 2: val_loss improved from 0.01006 to 0.00765, saving model to checkpoint1.hdf5
27/27 [==============================] - 9s 325ms/step - loss: 0.0077 - accuracy: 0.6893 - val_loss: 0.0077 - val_accuracy: 0.4322
Epoch 3/60
27/27 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 0.7179
Epoch 3: val_loss did not improve from 0.00765
27/27 [==============================] - 8s 305ms/step - loss: 0.0058 - accuracy: 0.7179 - val_loss: 0.0078 - val_accuracy: 0.4322
Epoch 4/60
27/27 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.7290
Epoch 4: val_loss did not improve from 0.00765
27/27 [==============================] - 8s 312ms/step - loss: 0.0053 - accuracy: 0.7290 - val_loss: 0.0079 - val_accuracy: 0.4322
Epoch 5/60
27/27 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.7371
Epoch 5: val_loss did not improve from 0.00765
27/27 [==============================] - 8s 312ms/step - loss: 0.0049 - accuracy: 0.7371 - val_loss: 0.0077 - val_accuracy: 0.4322
Epoch 6/60
27/27 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.7430
Epoch 6: val_loss improved from 0.00765 to 0.00756, saving model to checkpoint1.hdf5
27/27 [==============================] - 9s 330ms/step - loss: 0.0047 - accuracy: 0.7430 - val_loss: 0.0076 - val_accuracy: 0.4322
Epoch 7/60
27/27 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.7436
Epoch 7: val_loss improved from 0.00756 to 0.00738, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 317ms/step - loss: 0.0044 - accuracy: 0.7436 - val_loss: 0.0074 - val_accuracy: 0.4322
Epoch 8/60
27/27 [==============================] - ETA: 0s - loss: 0.0040 - accuracy: 0.7418
Epoch 8: val_loss did not improve from 0.00738
27/27 [==============================] - 8s 290ms/step - loss: 0.0040 - accuracy: 0.7418 - val_loss: 0.0074 - val_accuracy: 0.4322
Epoch 9/60
27/27 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.7617
Epoch 9: val_loss improved from 0.00738 to 0.00683, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 294ms/step - loss: 0.0038 - accuracy: 0.7617 - val_loss: 0.0068 - val_accuracy: 0.4299
Epoch 10/60
27/27 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.7582
Epoch 10: val_loss improved from 0.00683 to 0.00654, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 302ms/step - loss: 0.0033 - accuracy: 0.7582 - val_loss: 0.0065 - val_accuracy: 0.4322
Epoch 11/60
27/27 [==============================] - ETA: 0s - loss: 0.0029 - accuracy: 0.7558
Epoch 11: val_loss improved from 0.00654 to 0.00606, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 293ms/step - loss: 0.0029 - accuracy: 0.7558 - val_loss: 0.0061 - val_accuracy: 0.4369
Epoch 12/60
27/27 [==============================] - ETA: 0s - loss: 0.0027 - accuracy: 0.7605
Epoch 12: val_loss improved from 0.00606 to 0.00595, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 307ms/step - loss: 0.0027 - accuracy: 0.7605 - val_loss: 0.0059 - val_accuracy: 0.4369
Epoch 13/60
27/27 [==============================] - ETA: 0s - loss: 0.0026 - accuracy: 0.7704
Epoch 13: val_loss improved from 0.00595 to 0.00585, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 305ms/step - loss: 0.0026 - accuracy: 0.7704 - val_loss: 0.0059 - val_accuracy: 0.4416
Epoch 14/60
27/27 [==============================] - ETA: 0s - loss: 0.0024 - accuracy: 0.8002
Epoch 14: val_loss improved from 0.00585 to 0.00499, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 298ms/step - loss: 0.0024 - accuracy: 0.8002 - val_loss: 0.0050 - val_accuracy: 0.4486
Epoch 15/60
27/27 [==============================] - ETA: 0s - loss: 0.0022 - accuracy: 0.7845
Epoch 15: val_loss improved from 0.00499 to 0.00491, saving model to checkpoint1.hdf5
27/27 [==============================] - 9s 333ms/step - loss: 0.0022 - accuracy: 0.7845 - val_loss: 0.0049 - val_accuracy: 0.4533
Epoch 16/60
27/27 [==============================] - ETA: 0s - loss: 0.0022 - accuracy: 0.7886
Epoch 16: val_loss improved from 0.00491 to 0.00479, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 285ms/step - loss: 0.0022 - accuracy: 0.7886 - val_loss: 0.0048 - val_accuracy: 0.4673
Epoch 17/60
27/27 [==============================] - ETA: 0s - loss: 0.0019 - accuracy: 0.8002
Epoch 17: val_loss improved from 0.00479 to 0.00441, saving model to checkpoint1.hdf5
27/27 [==============================] - 9s 318ms/step - loss: 0.0019 - accuracy: 0.8002 - val_loss: 0.0044 - val_accuracy: 0.4720
Epoch 18/60
27/27 [==============================] - ETA: 0s - loss: 0.0019 - accuracy: 0.8178
Epoch 18: val_loss improved from 0.00441 to 0.00439, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 311ms/step - loss: 0.0019 - accuracy: 0.8178 - val_loss: 0.0044 - val_accuracy: 0.4790
Epoch 19/60
27/27 [==============================] - ETA: 0s - loss: 0.0018 - accuracy: 0.8037
Epoch 19: val_loss improved from 0.00439 to 0.00402, saving model to checkpoint1.hdf5
27/27 [==============================] - 9s 316ms/step - loss: 0.0018 - accuracy: 0.8037 - val_loss: 0.0040 - val_accuracy: 0.4860
Epoch 20/60
27/27 [==============================] - ETA: 0s - loss: 0.0017 - accuracy: 0.8055
Epoch 20: val_loss did not improve from 0.00402
27/27 [==============================] - 8s 297ms/step - loss: 0.0017 - accuracy: 0.8055 - val_loss: 0.0041 - val_accuracy: 0.4790
Epoch 21/60
27/27 [==============================] - ETA: 0s - loss: 0.0016 - accuracy: 0.8189
Epoch 21: val_loss improved from 0.00402 to 0.00399, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 305ms/step - loss: 0.0016 - accuracy: 0.8189 - val_loss: 0.0040 - val_accuracy: 0.5257
Epoch 22/60
27/27 [==============================] - ETA: 0s - loss: 0.0016 - accuracy: 0.8236
Epoch 22: val_loss improved from 0.00399 to 0.00389, saving model to checkpoint1.hdf5
27/27 [==============================] - 9s 327ms/step - loss: 0.0016 - accuracy: 0.8236 - val_loss: 0.0039 - val_accuracy: 0.4907
Epoch 23/60
27/27 [==============================] - ETA: 0s - loss: 0.0015 - accuracy: 0.8318
Epoch 23: val_loss improved from 0.00389 to 0.00384, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 307ms/step - loss: 0.0015 - accuracy: 0.8318 - val_loss: 0.0038 - val_accuracy: 0.4883
Epoch 24/60
27/27 [==============================] - ETA: 0s - loss: 0.0014 - accuracy: 0.8341
Epoch 24: val_loss improved from 0.00384 to 0.00382, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 304ms/step - loss: 0.0014 - accuracy: 0.8341 - val_loss: 0.0038 - val_accuracy: 0.5280
Epoch 25/60
27/27 [==============================] - ETA: 0s - loss: 0.0015 - accuracy: 0.8405
Epoch 25: val_loss did not improve from 0.00382
27/27 [==============================] - 8s 298ms/step - loss: 0.0015 - accuracy: 0.8405 - val_loss: 0.0039 - val_accuracy: 0.5023
Epoch 26/60
27/27 [==============================] - ETA: 0s - loss: 0.0014 - accuracy: 0.8359
Epoch 26: val_loss improved from 0.00382 to 0.00363, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 308ms/step - loss: 0.0014 - accuracy: 0.8359 - val_loss: 0.0036 - val_accuracy: 0.4977
Epoch 27/60
27/27 [==============================] - ETA: 0s - loss: 0.0014 - accuracy: 0.8435
Epoch 27: val_loss did not improve from 0.00363
27/27 [==============================] - 8s 309ms/step - loss: 0.0014 - accuracy: 0.8435 - val_loss: 0.0039 - val_accuracy: 0.5070
Epoch 28/60
27/27 [==============================] - ETA: 0s - loss: 0.0014 - accuracy: 0.8353
Epoch 28: val_loss did not improve from 0.00363
27/27 [==============================] - 8s 306ms/step - loss: 0.0014 - accuracy: 0.8353 - val_loss: 0.0039 - val_accuracy: 0.5304
Epoch 29/60
27/27 [==============================] - ETA: 0s - loss: 0.0013 - accuracy: 0.8341
Epoch 29: val_loss improved from 0.00363 to 0.00356, saving model to checkpoint1.hdf5
27/27 [==============================] - 9s 318ms/step - loss: 0.0013 - accuracy: 0.8341 - val_loss: 0.0036 - val_accuracy: 0.5257
Epoch 30/60
27/27 [==============================] - ETA: 0s - loss: 0.0012 - accuracy: 0.8528
Epoch 30: val_loss improved from 0.00356 to 0.00351, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 301ms/step - loss: 0.0012 - accuracy: 0.8528 - val_loss: 0.0035 - val_accuracy: 0.5070
Epoch 31/60
27/27 [==============================] - ETA: 0s - loss: 0.0012 - accuracy: 0.8423
Epoch 31: val_loss did not improve from 0.00351
27/27 [==============================] - 8s 299ms/step - loss: 0.0012 - accuracy: 0.8423 - val_loss: 0.0036 - val_accuracy: 0.5023
Epoch 32/60
27/27 [==============================] - ETA: 0s - loss: 0.0012 - accuracy: 0.8511
Epoch 32: val_loss did not improve from 0.00351
27/27 [==============================] - 8s 309ms/step - loss: 0.0012 - accuracy: 0.8511 - val_loss: 0.0036 - val_accuracy: 0.4930
Epoch 33/60
27/27 [==============================] - ETA: 0s - loss: 0.0013 - accuracy: 0.8475
Epoch 33: val_loss did not improve from 0.00351
27/27 [==============================] - 8s 308ms/step - loss: 0.0013 - accuracy: 0.8475 - val_loss: 0.0036 - val_accuracy: 0.5047
Epoch 34/60
27/27 [==============================] - ETA: 0s - loss: 0.0011 - accuracy: 0.8458
Epoch 34: val_loss improved from 0.00351 to 0.00349, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 302ms/step - loss: 0.0011 - accuracy: 0.8458 - val_loss: 0.0035 - val_accuracy: 0.5397
Epoch 35/60
27/27 [==============================] - ETA: 0s - loss: 0.0011 - accuracy: 0.8563
Epoch 35: val_loss improved from 0.00349 to 0.00337, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 287ms/step - loss: 0.0011 - accuracy: 0.8563 - val_loss: 0.0034 - val_accuracy: 0.5187
Epoch 36/60
27/27 [==============================] - ETA: 0s - loss: 0.0011 - accuracy: 0.8516
Epoch 36: val_loss did not improve from 0.00337
27/27 [==============================] - 8s 298ms/step - loss: 0.0011 - accuracy: 0.8516 - val_loss: 0.0035 - val_accuracy: 0.5257
Epoch 37/60
27/27 [==============================] - ETA: 0s - loss: 0.0011 - accuracy: 0.8715
Epoch 37: val_loss did not improve from 0.00337
27/27 [==============================] - 8s 308ms/step - loss: 0.0011 - accuracy: 0.8715 - val_loss: 0.0039 - val_accuracy: 0.5421
Epoch 38/60
27/27 [==============================] - ETA: 0s - loss: 0.0012 - accuracy: 0.8569
Epoch 38: val_loss did not improve from 0.00337
27/27 [==============================] - 9s 317ms/step - loss: 0.0012 - accuracy: 0.8569 - val_loss: 0.0034 - val_accuracy: 0.4930
Epoch 39/60
27/27 [==============================] - ETA: 0s - loss: 0.0010 - accuracy: 0.8557
Epoch 39: val_loss did not improve from 0.00337
27/27 [==============================] - 8s 291ms/step - loss: 0.0010 - accuracy: 0.8557 - val_loss: 0.0034 - val_accuracy: 0.5257
Epoch 40/60
27/27 [==============================] - ETA: 0s - loss: 9.9151e-04 - accuracy: 0.8528
Epoch 40: val_loss improved from 0.00337 to 0.00333, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 304ms/step - loss: 9.9151e-04 - accuracy: 0.8528 - val_loss: 0.0033 - val_accuracy: 0.5304
Epoch 41/60
27/27 [==============================] - ETA: 0s - loss: 9.7985e-04 - accuracy: 0.8610
Epoch 41: val_loss did not improve from 0.00333
27/27 [==============================] - 9s 317ms/step - loss: 9.7985e-04 - accuracy: 0.8610 - val_loss: 0.0034 - val_accuracy: 0.5514
Epoch 42/60
27/27 [==============================] - ETA: 0s - loss: 9.7914e-04 - accuracy: 0.8639
Epoch 42: val_loss did not improve from 0.00333
27/27 [==============================] - 8s 287ms/step - loss: 9.7914e-04 - accuracy: 0.8639 - val_loss: 0.0035 - val_accuracy: 0.5234
Epoch 43/60
27/27 [==============================] - ETA: 0s - loss: 9.5562e-04 - accuracy: 0.8651
Epoch 43: val_loss did not improve from 0.00333
27/27 [==============================] - 8s 296ms/step - loss: 9.5562e-04 - accuracy: 0.8651 - val_loss: 0.0034 - val_accuracy: 0.5164
Epoch 44/60
27/27 [==============================] - ETA: 0s - loss: 9.6482e-04 - accuracy: 0.8516
Epoch 44: val_loss did not improve from 0.00333
27/27 [==============================] - 9s 325ms/step - loss: 9.6482e-04 - accuracy: 0.8516 - val_loss: 0.0034 - val_accuracy: 0.5491
Epoch 45/60
27/27 [==============================] - ETA: 0s - loss: 9.3110e-04 - accuracy: 0.8674
Epoch 45: val_loss improved from 0.00333 to 0.00325, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 315ms/step - loss: 9.3110e-04 - accuracy: 0.8674 - val_loss: 0.0033 - val_accuracy: 0.5304
Epoch 46/60
27/27 [==============================] - ETA: 0s - loss: 9.2465e-04 - accuracy: 0.8762
Epoch 46: val_loss did not improve from 0.00325
27/27 [==============================] - 9s 321ms/step - loss: 9.2465e-04 - accuracy: 0.8762 - val_loss: 0.0034 - val_accuracy: 0.5748
Epoch 47/60
27/27 [==============================] - ETA: 0s - loss: 9.1168e-04 - accuracy: 0.8674
Epoch 47: val_loss did not improve from 0.00325
27/27 [==============================] - 8s 310ms/step - loss: 9.1168e-04 - accuracy: 0.8674 - val_loss: 0.0033 - val_accuracy: 0.5421
Epoch 48/60
27/27 [==============================] - ETA: 0s - loss: 8.7742e-04 - accuracy: 0.8674
Epoch 48: val_loss did not improve from 0.00325
27/27 [==============================] - 8s 304ms/step - loss: 8.7742e-04 - accuracy: 0.8674 - val_loss: 0.0033 - val_accuracy: 0.5537
Epoch 49/60
27/27 [==============================] - ETA: 0s - loss: 9.0614e-04 - accuracy: 0.8756
Epoch 49: val_loss did not improve from 0.00325
27/27 [==============================] - 8s 308ms/step - loss: 9.0614e-04 - accuracy: 0.8756 - val_loss: 0.0033 - val_accuracy: 0.5187
Epoch 50/60
27/27 [==============================] - ETA: 0s - loss: 8.5400e-04 - accuracy: 0.8703
Epoch 50: val_loss improved from 0.00325 to 0.00317, saving model to checkpoint1.hdf5
27/27 [==============================] - 9s 319ms/step - loss: 8.5400e-04 - accuracy: 0.8703 - val_loss: 0.0032 - val_accuracy: 0.5350
Epoch 51/60
27/27 [==============================] - ETA: 0s - loss: 8.2141e-04 - accuracy: 0.8785
Epoch 51: val_loss did not improve from 0.00317
27/27 [==============================] - 8s 309ms/step - loss: 8.2141e-04 - accuracy: 0.8785 - val_loss: 0.0034 - val_accuracy: 0.5794
Epoch 52/60
27/27 [==============================] - ETA: 0s - loss: 8.6456e-04 - accuracy: 0.8703
Epoch 52: val_loss did not improve from 0.00317
27/27 [==============================] - 8s 303ms/step - loss: 8.6456e-04 - accuracy: 0.8703 - val_loss: 0.0036 - val_accuracy: 0.5607
Epoch 53/60
27/27 [==============================] - ETA: 0s - loss: 9.1109e-04 - accuracy: 0.8732
Epoch 53: val_loss did not improve from 0.00317
27/27 [==============================] - 8s 298ms/step - loss: 9.1109e-04 - accuracy: 0.8732 - val_loss: 0.0034 - val_accuracy: 0.5514
Epoch 54/60
27/27 [==============================] - ETA: 0s - loss: 7.9785e-04 - accuracy: 0.8855
Epoch 54: val_loss did not improve from 0.00317
27/27 [==============================] - 8s 301ms/step - loss: 7.9785e-04 - accuracy: 0.8855 - val_loss: 0.0033 - val_accuracy: 0.5491
Epoch 55/60
27/27 [==============================] - ETA: 0s - loss: 7.7316e-04 - accuracy: 0.8773
Epoch 55: val_loss did not improve from 0.00317
27/27 [==============================] - 8s 291ms/step - loss: 7.7316e-04 - accuracy: 0.8773 - val_loss: 0.0032 - val_accuracy: 0.5841
Epoch 56/60
27/27 [==============================] - ETA: 0s - loss: 8.1521e-04 - accuracy: 0.8732
Epoch 56: val_loss did not improve from 0.00317
27/27 [==============================] - 8s 302ms/step - loss: 8.1521e-04 - accuracy: 0.8732 - val_loss: 0.0034 - val_accuracy: 0.5537
Epoch 57/60
27/27 [==============================] - ETA: 0s - loss: 8.2878e-04 - accuracy: 0.8797
Epoch 57: val_loss did not improve from 0.00317
27/27 [==============================] - 8s 280ms/step - loss: 8.2878e-04 - accuracy: 0.8797 - val_loss: 0.0035 - val_accuracy: 0.5187
Epoch 58/60
27/27 [==============================] - ETA: 0s - loss: 7.6416e-04 - accuracy: 0.8709
Epoch 58: val_loss did not improve from 0.00317
27/27 [==============================] - 8s 311ms/step - loss: 7.6416e-04 - accuracy: 0.8709 - val_loss: 0.0032 - val_accuracy: 0.5467
Epoch 59/60
27/27 [==============================] - ETA: 0s - loss: 7.5469e-04 - accuracy: 0.8738
Epoch 59: val_loss improved from 0.00317 to 0.00315, saving model to checkpoint1.hdf5
27/27 [==============================] - 8s 306ms/step - loss: 7.5469e-04 - accuracy: 0.8738 - val_loss: 0.0031 - val_accuracy: 0.5678
Epoch 60/60
27/27 [==============================] - ETA: 0s - loss: 7.5889e-04 - accuracy: 0.8843
Epoch 60: val_loss did not improve from 0.00315
27/27 [==============================] - 8s 281ms/step - loss: 7.5889e-04 - accuracy: 0.8843 - val_loss: 0.0032 - val_accuracy: 0.5467

# Plot train and validation loss
plt.plot(hist.history['loss'], label='Train Loss')
plt.plot(hist.history['val_loss'], label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

Test image file with the model

def detect_points(face_img):
    me  = np.array(face_img)/255
    x_test = np.expand_dims(me, axis=0)
    x_test = np.expand_dims(x_test, axis=3)

    y_test = model.predict(x_test)
    label_points = (np.squeeze(y_test)*48)+48 
    
    return label_points

cv2.CascadeClassifier
# Load haarcascade
face_cascade = cv2.CascadeClassifier('C:\\Users\\PRITI CHAUDHARY\\Python\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_default.xml')
dimensions = (96, 96)

# Enter the path to the test image
img = cv2.imread('trio.jpeg')
default_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
plt.imshow(default_img)

<matplotlib.image.AxesImage at 0x20791aafbb0>

gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.imshow(gray_img)

<matplotlib.image.AxesImage at 0x20791a7fb50>

faces = face_cascade.detectMultiScale(gray_img, 1.3, 5)
faces_img = np.copy(gray_img)
plt.rcParams["axes.grid"] = False

all_x_cords = []
all_y_cords = []

for i, (x,y,w,h) in enumerate(faces):
    
    h += 10
    w += 10
    x -= 5
    y -= 5
    
    just_face = cv2.resize(gray_img[y:y+h,x:x+w], dimensions)
    cv2.rectangle(faces_img,(x,y),(x+w,y+h),(255,0,0),1)
    
    scale_val_x = w/96
    scale_val_y = h/96
    
    label_point = detect_points(just_face)
    all_x_cords.append((label_point[::2]*scale_val_x)+x)
    all_y_cords.append((label_point[1::2]*scale_val_y)+y)
   
   
    plt.imshow(just_face, cmap='gray')
    plt.plot(label_point[::2], label_point[1::2], 'ro', markersize=5)
    plt.show()
    
    
plt.imshow(default_img)    
plt.plot(all_x_cords, all_y_cords, 'wo',  markersize=1)
plt.show()

1/1 [==============================] - 0s 156ms/step

1/1 [==============================] - 0s 31ms/step

1/1 [==============================] - 0s 47ms/step

1/1 [==============================] - 0s 31ms/step

Thank you:)